R Notebook: estimation of gas costs using the measure marginal method; for Gas Cost Estimator; for a single given env
In this script we conduct the estimation for the
measure_marginal approach for a single given env =
revm.
PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.
Expected a result file revm_pg_marginal_full5_c50_step1_shuffle_50_4.csv.
programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))
results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]
all_envs = c(env)measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)## opcode op_count sample_id run_id measure_total_time_ns env program_id
## 1 ADD 27 0 1 581 revm ADD_27
## 2 ADD 27 0 2 585 revm ADD_27
## 3 ADD 27 0 3 633 revm ADD_27
## 4 ADD 27 0 4 610 revm ADD_27
## 5 ADD 27 0 5 563 revm ADD_27
## 6 ADD 27 1 1 588 revm ADD_27Switch removed_outliers to FALSE to see the
comparison.
boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
if (removed_outliers) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}
# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
mix_model = normalmixEM(df$measure_total_time_ns)
print(summary(mix_model))
plot(mix_model,which=2)
mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
over_threshold = which(df$measure_total_time_ns > mode_midpoint)
df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
return(df)
}
# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
if (missing(bimodal_opcodes)) {
bimodal_opcodes = c()
}
if (missing(plots)) {
plots = "scatter"
}
if (missing(use_median)) {
use_median = FALSE
}
print(c(opcode, env))
df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
if (opcode %in% bimodal_opcodes) {
par(mfrow=c(1,2))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
title(main=paste(env, opcode))
# correct_bimodal plots the second plot inside
df = correct_bimodal(df)
}
if (use_median) {
f = median
} else {
f = mean
}
df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
print(summary(model_mean))
slope = model_mean$coefficients[['op_count']]
stderr = summary(model_mean)$coefficients['op_count','Std. Error']
if (plots == "scatter" | plots == "all") {
par(mfrow=c(1,1))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
rounded_slope = round(slope, 3)
rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
rounded_stderr = round(stderr, 3)
title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
abline(model_mean, col="red")
}
if (plots == "diagnostics" | plots == "all") {
par(mfrow=c(2,2))
plot(model_mean)
}
list("slope" = slope, "stderr" = stderr)
}
extract_opcodes <- function() {
unique(measurements$opcode)
}all_opcodes = extract_opcodes()
# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.
for (opcode in all_opcodes) {
boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}
Now we can investigate the linear regressions.
if (env == 'evmone') {
bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
bimodals = c()
}
for (opcode in all_opcodes) {
estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}## [1] "ADD" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.121 -1.291 1.827 4.709 12.590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.12104 2.60124 199.57 <0.0000000000000002 ***
## op_count 2.06575 0.08966 23.04 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.425 on 49 degrees of freedom
## Multiple R-squared: 0.9155, Adjusted R-squared: 0.9138
## F-statistic: 530.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MUL" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.173 -3.203 3.046 6.048 18.215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 520.17270 2.75056 189.12 <0.0000000000000002 ***
## op_count 3.47231 0.09481 36.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.966 on 49 degrees of freedom
## Multiple R-squared: 0.9648, Adjusted R-squared: 0.964
## F-statistic: 1341 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SUB" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.308 -2.477 2.079 5.621 11.460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 517.16139 2.48809 207.85 <0.0000000000000002 ***
## op_count 2.14688 0.08576 25.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.015 on 49 degrees of freedom
## Multiple R-squared: 0.9275, Adjusted R-squared: 0.926
## F-statistic: 626.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DIV" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.550 -5.052 -0.619 2.111 33.073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.4770 3.3101 156.94 <0.0000000000000002 ***
## op_count 8.0731 0.1141 70.76 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.99 on 49 degrees of freedom
## Multiple R-squared: 0.9903, Adjusted R-squared: 0.9901
## F-statistic: 5007 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SDIV" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.709 -3.424 -0.530 1.984 21.796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 498.14367 1.56921 317.4 <0.0000000000000002 ***
## op_count 14.32367 0.05409 264.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.686 on 49 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 7.013e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MOD" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.109 -6.157 -1.000 3.456 32.264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 517.6086 3.1497 164.33 <0.0000000000000002 ***
## op_count 8.3627 0.1086 77.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.41 on 49 degrees of freedom
## Multiple R-squared: 0.9918, Adjusted R-squared: 0.9916
## F-statistic: 5933 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SMOD" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.1830 -4.7095 -0.2704 2.4026 21.3857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 499.20023 1.88125 265.4 <0.0000000000000002 ***
## op_count 10.24140 0.06485 157.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.816 on 49 degrees of freedom
## Multiple R-squared: 0.998, Adjusted R-squared: 0.998
## F-statistic: 2.494e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDMOD" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0066 -2.7065 0.7806 3.0773 9.1612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 493.88386 1.25811 392.6 <0.0000000000000002 ***
## op_count 15.15484 0.04337 349.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.559 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.221e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MULMOD" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.490 -2.635 2.560 5.116 10.887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.49020 2.13264 243.6 <0.0000000000000002 ***
## op_count 17.94471 0.07351 244.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.727 on 49 degrees of freedom
## Multiple R-squared: 0.9992, Adjusted R-squared: 0.9992
## F-statistic: 5.959e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EXP" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.8803 -3.1785 0.7508 5.5747 17.8022
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 503.28130 2.11720 237.7 <0.0000000000000002 ***
## op_count 18.74679 0.07298 256.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.671 on 49 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9992
## F-statistic: 6.599e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SIGNEXTEND" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0965 -8.8114 0.9281 5.6708 24.9158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 510.09653 2.87631 177.3 <0.0000000000000002 ***
## op_count 6.99928 0.09914 70.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.42 on 49 degrees of freedom
## Multiple R-squared: 0.9903, Adjusted R-squared: 0.9901
## F-statistic: 4984 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "LT" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.045 -1.169 1.037 4.981 10.807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 520.83861 2.37480 219.32 <0.0000000000000002 ***
## op_count 2.20606 0.08186 26.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.605 on 49 degrees of freedom
## Multiple R-squared: 0.9368, Adjusted R-squared: 0.9355
## F-statistic: 726.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GT" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.847 -1.133 1.891 4.252 10.942
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 517.84691 2.20180 235.19 <0.0000000000000002 ***
## op_count 2.27005 0.07589 29.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.978 on 49 degrees of freedom
## Multiple R-squared: 0.9481, Adjusted R-squared: 0.947
## F-statistic: 894.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SLT" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.962 -3.411 1.838 4.989 10.539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 514.46192 2.14295 240.07 <0.0000000000000002 ***
## op_count 3.79995 0.07387 51.44 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.765 on 49 degrees of freedom
## Multiple R-squared: 0.9818, Adjusted R-squared: 0.9815
## F-statistic: 2647 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SGT" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.647 -2.117 2.093 4.423 11.973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 517.64706 2.38730 216.83 <0.0000000000000002 ***
## op_count 3.78000 0.08229 45.94 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.65 on 49 degrees of freedom
## Multiple R-squared: 0.9773, Adjusted R-squared: 0.9768
## F-statistic: 2110 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EQ" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.930 -2.431 2.613 5.018 12.658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 521.0038 2.5415 205.00 <0.0000000000000002 ***
## op_count 1.9265 0.0876 21.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.209 on 49 degrees of freedom
## Multiple R-squared: 0.908, Adjusted R-squared: 0.9061
## F-statistic: 483.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ISZERO" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.9721 -3.3104 0.7074 5.3546 13.7170
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 513.97210 2.15655 238.33 <0.0000000000000002 ***
## op_count 1.83014 0.07433 24.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.814 on 49 degrees of freedom
## Multiple R-squared: 0.9252, Adjusted R-squared: 0.9237
## F-statistic: 606.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "AND" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.876 -2.347 2.279 4.566 18.726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.87632 2.59596 200.26 <0.0000000000000002 ***
## op_count 1.67475 0.08948 18.72 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.406 on 49 degrees of freedom
## Multiple R-squared: 0.8773, Adjusted R-squared: 0.8748
## F-statistic: 350.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "OR" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.045 -2.106 2.777 6.017 12.258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 518.37443 2.77529 186.78 <0.0000000000000002 ***
## op_count 1.67090 0.09566 17.47 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.06 on 49 degrees of freedom
## Multiple R-squared: 0.8616, Adjusted R-squared: 0.8588
## F-statistic: 305.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "XOR" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.457 -2.907 2.056 5.328 16.796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 520.836 2.727 190.99 <0.0000000000000002 ***
## op_count 1.620 0.094 17.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.881 on 49 degrees of freedom
## Multiple R-squared: 0.8584, Adjusted R-squared: 0.8555
## F-statistic: 297.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "NOT" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.339 -1.672 2.108 5.665 12.789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 515.89894 2.60336 198.17 <0.0000000000000002 ***
## op_count 1.43973 0.08974 16.04 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.433 on 49 degrees of freedom
## Multiple R-squared: 0.8401, Adjusted R-squared: 0.8368
## F-statistic: 257.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "BYTE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.1018 -8.9156 -0.0614 4.8837 30.7727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 518.602 3.860 134.4 <0.0000000000000002 ***
## op_count 5.535 0.133 41.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.99 on 49 degrees of freedom
## Multiple R-squared: 0.9725, Adjusted R-squared: 0.9719
## F-statistic: 1731 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SHL" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.516 -10.291 -5.858 17.772 29.504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 539.9593 5.4893 98.37 <0.0000000000000002 ***
## op_count 6.0569 0.1892 32.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.89 on 49 degrees of freedom
## Multiple R-squared: 0.9544, Adjusted R-squared: 0.9534
## F-statistic: 1025 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SHR" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.956 -6.963 -1.781 5.212 34.825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 523.3171 4.3985 118.98 <0.0000000000000002 ***
## op_count 7.6387 0.1516 50.38 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.94 on 49 degrees of freedom
## Multiple R-squared: 0.9811, Adjusted R-squared: 0.9807
## F-statistic: 2538 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SAR" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.074 -5.192 -2.758 2.818 27.921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 503.52828 2.43796 206.5 <0.0000000000000002 ***
## op_count 10.04593 0.08403 119.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.834 on 49 degrees of freedom
## Multiple R-squared: 0.9966, Adjusted R-squared: 0.9965
## F-statistic: 1.429e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDRESS" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.705 -1.856 2.833 5.375 9.753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.8940 2.4020 94.46 <0.0000000000000002 ***
## op_count 1.8109 0.0828 21.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.703 on 49 degrees of freedom
## Multiple R-squared: 0.9071, Adjusted R-squared: 0.9052
## F-statistic: 478.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ORIGIN" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.698 -1.348 3.328 5.359 8.932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.70626 2.37148 95.17 <0.0000000000000002 ***
## op_count 1.99136 0.08174 24.36 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.593 on 49 degrees of freedom
## Multiple R-squared: 0.9237, Adjusted R-squared: 0.9222
## F-statistic: 593.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLER" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.323 -1.944 3.422 6.291 8.644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 228.32278 2.55540 89.35 <0.0000000000000002 ***
## op_count 1.83611 0.08808 20.84 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.259 on 49 degrees of freedom
## Multiple R-squared: 0.8987, Adjusted R-squared: 0.8966
## F-statistic: 434.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLVALUE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.748 -1.372 2.525 5.013 9.735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.74774 2.57177 88.56 <0.0000000000000002 ***
## op_count 1.94538 0.08865 21.95 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.318 on 49 degrees of freedom
## Multiple R-squared: 0.9077, Adjusted R-squared: 0.9058
## F-statistic: 481.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATALOAD" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.302 -11.395 0.417 14.068 35.258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 792.3017 4.8360 163.84 <0.0000000000000002 ***
## op_count 2.9801 0.1667 17.88 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.52 on 49 degrees of freedom
## Multiple R-squared: 0.8671, Adjusted R-squared: 0.8644
## F-statistic: 319.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATASIZE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.7357 -0.7218 2.3999 5.1869 9.1495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.73567 2.41254 94.40 <0.0000000000000002 ***
## op_count 1.58783 0.08316 19.09 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.741 on 49 degrees of freedom
## Multiple R-squared: 0.8815, Adjusted R-squared: 0.8791
## F-statistic: 364.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATACOPY" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.974 -7.694 -0.370 6.433 37.010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 508.701 2.929 173.67 <0.0000000000000002 ***
## op_count 5.152 0.101 51.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.61 on 49 degrees of freedom
## Multiple R-squared: 0.9815, Adjusted R-squared: 0.9811
## F-statistic: 2603 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CODESIZE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.489 -1.339 2.311 5.147 10.625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.98944 2.40859 93.83 <0.0000000000000002 ***
## op_count 1.67140 0.08302 20.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.727 on 49 degrees of freedom
## Multiple R-squared: 0.8921, Adjusted R-squared: 0.8899
## F-statistic: 405.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CODECOPY" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.731 -5.514 -1.131 3.252 27.835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 520.76508 2.31876 224.59 <0.0000000000000002 ***
## op_count 6.76665 0.07993 84.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.402 on 49 degrees of freedom
## Multiple R-squared: 0.9932, Adjusted R-squared: 0.9931
## F-statistic: 7168 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASPRICE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.655 -1.324 2.770 5.262 10.783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.65083 2.44318 92.36 <0.0000000000000002 ***
## op_count 2.00416 0.08421 23.80 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.853 on 49 degrees of freedom
## Multiple R-squared: 0.9204, Adjusted R-squared: 0.9187
## F-statistic: 566.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "RETURNDATASIZE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.272 -1.286 3.137 5.015 10.257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.66365 2.56675 88.70 <0.0000000000000002 ***
## op_count 1.60796 0.08847 18.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.3 on 49 degrees of freedom
## Multiple R-squared: 0.8708, Adjusted R-squared: 0.8682
## F-statistic: 330.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "COINBASE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.451 -1.148 1.582 5.153 10.071
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.61840 2.37815 95.29 <0.0000000000000002 ***
## op_count 1.83213 0.08197 22.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.617 on 49 degrees of freedom
## Multiple R-squared: 0.9107, Adjusted R-squared: 0.9088
## F-statistic: 499.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "TIMESTAMP" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.788 -1.344 2.398 4.808 8.187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.82428 2.17733 103.26 <0.0000000000000002 ***
## op_count 1.96389 0.07505 26.17 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.889 on 49 degrees of freedom
## Multiple R-squared: 0.9332, Adjusted R-squared: 0.9319
## F-statistic: 684.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "NUMBER" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.319 -1.115 3.463 5.213 10.994
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.35030 2.52599 89.61 <0.0000000000000002 ***
## op_count 1.96873 0.08707 22.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.153 on 49 degrees of freedom
## Multiple R-squared: 0.9125, Adjusted R-squared: 0.9108
## F-statistic: 511.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DIFFICULTY" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.975 -1.427 2.718 4.716 9.490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.87255 2.32526 97.57 <0.0000000000000002 ***
## op_count 2.10235 0.08015 26.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.425 on 49 degrees of freedom
## Multiple R-squared: 0.9335, Adjusted R-squared: 0.9322
## F-statistic: 688 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASLIMIT" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.8752 -0.5465 2.5777 5.0714 10.1531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.87519 2.51150 90.33 <0.0000000000000002 ***
## op_count 1.95597 0.08657 22.59 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.1 on 49 degrees of freedom
## Multiple R-squared: 0.9124, Adjusted R-squared: 0.9106
## F-statistic: 510.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CHAINID" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.929 -1.649 3.316 5.345 10.631
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.92911 2.55791 88.72 <0.0000000000000002 ***
## op_count 1.93774 0.08817 21.98 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.268 on 49 degrees of freedom
## Multiple R-squared: 0.9079, Adjusted R-squared: 0.906
## F-statistic: 483 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SELFBALANCE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.1909 -0.5871 2.0552 4.3245 12.5798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.56938 2.19888 102.13 <0.0000000000000002 ***
## op_count 1.62154 0.07579 21.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.967 on 49 degrees of freedom
## Multiple R-squared: 0.9033, Adjusted R-squared: 0.9013
## F-statistic: 457.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "POP" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9024 -2.6426 -0.0837 1.8050 7.2963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 317.28017 0.85989 368.98 <0.0000000000000002 ***
## op_count 1.33624 0.02964 45.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.116 on 49 degrees of freedom
## Multiple R-squared: 0.9765, Adjusted R-squared: 0.976
## F-statistic: 2032 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MLOAD" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.090 -10.175 0.051 10.930 26.981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 784.9099 4.3012 182.49 <0.0000000000000002 ***
## op_count 6.1797 0.1483 41.68 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.58 on 49 degrees of freedom
## Multiple R-squared: 0.9726, Adjusted R-squared: 0.972
## F-statistic: 1737 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.943 -8.515 1.290 6.228 29.142
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 512.8296 3.0684 167.1 <0.0000000000000002 ***
## op_count 3.4476 0.1058 32.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.12 on 49 degrees of freedom
## Multiple R-squared: 0.9559, Adjusted R-squared: 0.955
## F-statistic: 1063 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE8" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.1791 -7.8512 -0.6557 7.6705 28.7647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 506.6357 3.3110 153.02 <0.0000000000000002 ***
## op_count 2.9040 0.1141 25.45 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12 on 49 degrees of freedom
## Multiple R-squared: 0.9296, Adjusted R-squared: 0.9282
## F-statistic: 647.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMP" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.1452 -1.4236 0.2671 1.7929 10.2741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 195.14517 1.14211 170.86 <0.0000000000000002 ***
## op_count 2.31615 0.03937 58.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.138 on 49 degrees of freedom
## Multiple R-squared: 0.986, Adjusted R-squared: 0.9858
## F-statistic: 3461 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMPI" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.826 -10.544 -1.831 13.502 23.452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 520.8262 4.1957 124.13 <0.0000000000000002 ***
## op_count 2.7901 0.1446 19.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.2 on 49 degrees of freedom
## Multiple R-squared: 0.8837, Adjusted R-squared: 0.8813
## F-statistic: 372.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PC" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.706 -0.395 2.463 4.724 9.937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.1112 2.3876 95.12 <0.0000000000000002 ***
## op_count 1.5952 0.0823 19.38 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.651 on 49 degrees of freedom
## Multiple R-squared: 0.8846, Adjusted R-squared: 0.8823
## F-statistic: 375.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSIZE" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.998 -2.426 3.105 5.922 9.435
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.82353 2.56135 88.95 <0.0000000000000002 ***
## op_count 1.67412 0.08829 18.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.281 on 49 degrees of freedom
## Multiple R-squared: 0.8801, Adjusted R-squared: 0.8776
## F-statistic: 359.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GAS" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.3722 -0.7895 2.2842 5.0148 11.5395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.77413 2.33931 96.94 <0.0000000000000002 ***
## op_count 1.59805 0.08063 19.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.476 on 49 degrees of freedom
## Multiple R-squared: 0.8891, Adjusted R-squared: 0.8868
## F-statistic: 392.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMPDEST" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.833 -3.169 -0.034 1.421 47.719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.28054 2.04377 7.966 0.000000000215 ***
## op_count 1.09701 0.07045 15.572 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.405 on 49 degrees of freedom
## Multiple R-squared: 0.8319, Adjusted R-squared: 0.8285
## F-statistic: 242.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH1" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.077 -3.820 1.560 4.331 12.608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 205.89857 1.77408 116.06 <0.0000000000000002 ***
## op_count 1.53425 0.06115 25.09 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.428 on 49 degrees of freedom
## Multiple R-squared: 0.9278, Adjusted R-squared: 0.9263
## F-statistic: 629.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH2" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.388 -2.400 2.029 6.009 11.273
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.83107 2.45282 92.07 <0.0000000000000002 ***
## op_count 1.55656 0.08455 18.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.887 on 49 degrees of freedom
## Multiple R-squared: 0.8737, Adjusted R-squared: 0.8711
## F-statistic: 339 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH3" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.431 -2.450 2.863 6.201 11.054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.78620 2.60961 86.90 <0.0000000000000002 ***
## op_count 1.64502 0.08995 18.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.456 on 49 degrees of freedom
## Multiple R-squared: 0.8722, Adjusted R-squared: 0.8696
## F-statistic: 334.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH4" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.255 -1.631 3.264 5.615 11.504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.64932 2.46391 92.39 <0.0000000000000002 ***
## op_count 1.60579 0.08493 18.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.928 on 49 degrees of freedom
## Multiple R-squared: 0.8795, Adjusted R-squared: 0.877
## F-statistic: 357.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH5" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.714 -1.070 2.226 5.167 10.404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.06637 2.51094 90.43 <0.0000000000000002 ***
## op_count 1.64715 0.08655 19.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.098 on 49 degrees of freedom
## Multiple R-squared: 0.8808, Adjusted R-squared: 0.8784
## F-statistic: 362.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH6" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.514 -2.590 2.877 5.212 11.154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.87481 2.43061 93.34 <0.0000000000000002 ***
## op_count 1.63873 0.08378 19.56 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.807 on 49 degrees of freedom
## Multiple R-squared: 0.8865, Adjusted R-squared: 0.8841
## F-statistic: 382.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH7" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.363 -0.867 2.133 5.102 9.802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.60671 2.46705 92.26 <0.0000000000000002 ***
## op_count 1.75652 0.08504 20.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.939 on 49 degrees of freedom
## Multiple R-squared: 0.897, Adjusted R-squared: 0.8949
## F-statistic: 426.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH8" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.390 -0.732 1.901 5.649 10.398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.84276 2.54915 89.38 <0.0000000000000002 ***
## op_count 1.54747 0.08787 17.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.236 on 49 degrees of freedom
## Multiple R-squared: 0.8636, Adjusted R-squared: 0.8608
## F-statistic: 310.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH9" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.027 -1.491 2.774 6.157 11.152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.40988 2.43287 91.83 <0.0000000000000002 ***
## op_count 1.61733 0.08386 19.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.815 on 49 degrees of freedom
## Multiple R-squared: 0.8836, Adjusted R-squared: 0.8812
## F-statistic: 372 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH10" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.023 -4.333 3.359 5.811 9.955
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 228.38499 2.71567 84.1 <0.0000000000000002 ***
## op_count 1.63833 0.09361 17.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.84 on 49 degrees of freedom
## Multiple R-squared: 0.8621, Adjusted R-squared: 0.8593
## F-statistic: 306.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH11" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.184 -1.681 2.717 4.118 11.242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.42157 2.40630 94.09 <0.0000000000000002 ***
## op_count 1.76235 0.08294 21.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.719 on 49 degrees of freedom
## Multiple R-squared: 0.9021, Adjusted R-squared: 0.9001
## F-statistic: 451.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH12" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.261 -3.718 2.833 5.284 12.104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.7413 2.3412 96.85 <0.0000000000000002 ***
## op_count 1.5194 0.0807 18.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.483 on 49 degrees of freedom
## Multiple R-squared: 0.8786, Adjusted R-squared: 0.8761
## F-statistic: 354.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH13" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.962 -1.447 2.036 5.323 10.370
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.20211 2.35098 96.22 <0.0000000000000002 ***
## op_count 1.75937 0.08104 21.71 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.518 on 49 degrees of freedom
## Multiple R-squared: 0.9058, Adjusted R-squared: 0.9039
## F-statistic: 471.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH14" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.498 -1.754 2.509 5.779 10.276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.71078 2.44818 92.60 <0.0000000000000002 ***
## op_count 1.78765 0.08439 21.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.871 on 49 degrees of freedom
## Multiple R-squared: 0.9016, Adjusted R-squared: 0.8996
## F-statistic: 448.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH15" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.969 -1.701 2.390 5.620 10.151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.98906 2.51076 90.41 <0.0000000000000002 ***
## op_count 1.98005 0.08654 22.88 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.097 on 49 degrees of freedom
## Multiple R-squared: 0.9144, Adjusted R-squared: 0.9127
## F-statistic: 523.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH16" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.188 -1.343 2.381 5.387 8.872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.57994 2.40318 94.28 <0.0000000000000002 ***
## op_count 1.60778 0.08284 19.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.708 on 49 degrees of freedom
## Multiple R-squared: 0.8849, Adjusted R-squared: 0.8826
## F-statistic: 376.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH17" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.956 -1.093 1.732 5.943 10.132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 227.18514 2.51476 90.34 <0.0000000000000002 ***
## op_count 1.77063 0.08668 20.43 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.112 on 49 degrees of freedom
## Multiple R-squared: 0.8949, Adjusted R-squared: 0.8928
## F-statistic: 417.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH18" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.969 -2.667 1.481 5.295 12.158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.70098 2.44512 92.72 <0.0000000000000002 ***
## op_count 1.76765 0.08428 20.97 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.86 on 49 degrees of freedom
## Multiple R-squared: 0.8998, Adjusted R-squared: 0.8977
## F-statistic: 439.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH19" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.016 -1.583 2.122 4.427 10.760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.06222 2.11345 106.02 <0.0000000000000002 ***
## op_count 1.95398 0.07285 26.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.658 on 49 degrees of freedom
## Multiple R-squared: 0.9362, Adjusted R-squared: 0.9349
## F-statistic: 719.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH20" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.346 -2.926 2.182 5.293 11.685
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.56410 2.40172 93.92 <0.0000000000000002 ***
## op_count 1.78136 0.08279 21.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.702 on 49 degrees of freedom
## Multiple R-squared: 0.9043, Adjusted R-squared: 0.9023
## F-statistic: 463 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH21" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.215 -1.524 1.601 4.402 14.865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.29789 2.22271 100.91 <0.0000000000000002 ***
## op_count 1.91710 0.07661 25.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.054 on 49 degrees of freedom
## Multiple R-squared: 0.9274, Adjusted R-squared: 0.9259
## F-statistic: 626.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH22" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.713 -2.124 1.599 4.691 13.723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.77564 2.36613 95.00 <0.0000000000000002 ***
## op_count 1.93760 0.08156 23.76 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.573 on 49 degrees of freedom
## Multiple R-squared: 0.9201, Adjusted R-squared: 0.9185
## F-statistic: 564.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH23" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.449 -2.379 2.223 4.422 15.836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.48982 2.35138 95.90 <0.0000000000000002 ***
## op_count 1.95923 0.08105 24.17 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.52 on 49 degrees of freedom
## Multiple R-squared: 0.9226, Adjusted R-squared: 0.9211
## F-statistic: 584.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH24" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.928 -1.651 2.499 5.191 10.409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.92760 2.41719 93.88 <0.0000000000000002 ***
## op_count 1.80525 0.08332 21.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.758 on 49 degrees of freedom
## Multiple R-squared: 0.9055, Adjusted R-squared: 0.9036
## F-statistic: 469.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH25" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.244 -3.048 2.517 4.950 11.508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.36991 2.23326 100.47 <0.0000000000000002 ***
## op_count 1.87462 0.07698 24.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.092 on 49 degrees of freedom
## Multiple R-squared: 0.9237, Adjusted R-squared: 0.9221
## F-statistic: 593 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH26" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.145 -1.998 2.715 4.514 9.727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.62406 2.19385 102.39 <0.0000000000000002 ***
## op_count 2.02131 0.07562 26.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.949 on 49 degrees of freedom
## Multiple R-squared: 0.9358, Adjusted R-squared: 0.9345
## F-statistic: 714.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH27" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.863 -1.280 1.804 4.443 12.804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.80694 2.26160 99.40 <0.0000000000000002 ***
## op_count 2.05557 0.07796 26.37 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.195 on 49 degrees of freedom
## Multiple R-squared: 0.9342, Adjusted R-squared: 0.9328
## F-statistic: 695.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH28" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.544 -3.219 1.519 4.752 13.624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.57164 2.37095 94.72 <0.0000000000000002 ***
## op_count 1.97204 0.08172 24.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.591 on 49 degrees of freedom
## Multiple R-squared: 0.9224, Adjusted R-squared: 0.9208
## F-statistic: 582.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH29" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.385 -2.180 2.523 5.464 11.696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.31561 2.38070 94.64 <0.0000000000000002 ***
## op_count 2.06973 0.08206 25.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.626 on 49 degrees of freedom
## Multiple R-squared: 0.9285, Adjusted R-squared: 0.927
## F-statistic: 636.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH30" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.715 -1.859 2.118 4.931 11.822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.63763 2.47058 91.33 <0.0000000000000002 ***
## op_count 2.07724 0.08516 24.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.952 on 49 degrees of freedom
## Multiple R-squared: 0.9239, Adjusted R-squared: 0.9224
## F-statistic: 595 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH31" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.766 -2.552 2.312 5.422 13.228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226.07127 2.54357 88.88 <0.0000000000000002 ***
## op_count 2.19480 0.08767 25.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.216 on 49 degrees of freedom
## Multiple R-squared: 0.9275, Adjusted R-squared: 0.926
## F-statistic: 626.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH32" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.546 -1.925 2.542 4.547 13.473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.54864 2.24372 99.63 <0.0000000000000002 ***
## op_count 1.99688 0.07734 25.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.13 on 49 degrees of freedom
## Multiple R-squared: 0.9315, Adjusted R-squared: 0.9301
## F-statistic: 666.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP1" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.430 -1.652 2.168 5.620 11.755
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 516.83974 2.47344 208.96 <0.0000000000000002 ***
## op_count 1.59072 0.08526 18.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.962 on 49 degrees of freedom
## Multiple R-squared: 0.8766, Adjusted R-squared: 0.8741
## F-statistic: 348.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP2" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.065 -3.517 2.636 5.404 12.548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.42081 2.69662 192.62 <0.0000000000000002 ***
## op_count 1.64434 0.09295 17.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.771 on 49 degrees of freedom
## Multiple R-squared: 0.8646, Adjusted R-squared: 0.8619
## F-statistic: 313 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP3" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.929 -2.996 2.792 5.702 13.024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 518.92911 2.65958 195.12 <0.0000000000000002 ***
## op_count 1.64950 0.09167 17.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.637 on 49 degrees of freedom
## Multiple R-squared: 0.8685, Adjusted R-squared: 0.8659
## F-statistic: 323.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP4" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.242 -1.838 2.713 6.617 12.722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 520.24170 2.79124 186.38 <0.0000000000000002 ***
## op_count 1.58484 0.09621 16.47 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.11 on 49 degrees of freedom
## Multiple R-squared: 0.847, Adjusted R-squared: 0.8439
## F-statistic: 271.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP5" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.612 -2.122 2.410 5.208 15.939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.58258 2.64165 196.7 <0.0000000000000002 ***
## op_count 1.52964 0.09106 16.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.572 on 49 degrees of freedom
## Multiple R-squared: 0.8521, Adjusted R-squared: 0.849
## F-statistic: 282.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP6" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.064 -1.898 1.443 5.820 15.822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 521.44683 2.73141 190.91 <0.0000000000000002 ***
## op_count 1.61742 0.09415 17.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.897 on 49 degrees of freedom
## Multiple R-squared: 0.8576, Adjusted R-squared: 0.8547
## F-statistic: 295.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP7" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.094 -1.644 2.650 5.393 11.320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 525.53017 2.70722 194.12 <0.0000000000000002 ***
## op_count 1.56389 0.09332 16.76 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.809 on 49 degrees of freedom
## Multiple R-squared: 0.8515, Adjusted R-squared: 0.8484
## F-statistic: 280.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP8" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.908 -1.995 2.331 6.136 13.418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 525.38575 2.65117 198.17 <0.0000000000000002 ***
## op_count 1.63045 0.09138 17.84 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.606 on 49 degrees of freedom
## Multiple R-squared: 0.8666, Adjusted R-squared: 0.8639
## F-statistic: 318.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP9" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.131 -1.029 2.073 5.909 12.937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.50038 2.86215 181.51 <0.0000000000000002 ***
## op_count 1.63018 0.09866 16.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.37 on 49 degrees of freedom
## Multiple R-squared: 0.8478, Adjusted R-squared: 0.8447
## F-statistic: 273 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP10" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.959 -2.937 2.354 5.141 13.548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 518.31750 2.73706 189.4 <0.0000000000000002 ***
## op_count 1.64181 0.09434 17.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.917 on 49 degrees of freedom
## Multiple R-squared: 0.8607, Adjusted R-squared: 0.8579
## F-statistic: 302.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP11" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.011 -1.606 2.798 5.516 15.804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 533.41176 2.77220 192.41 <0.0000000000000002 ***
## op_count 1.59882 0.09556 16.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.04 on 49 degrees of freedom
## Multiple R-squared: 0.851, Adjusted R-squared: 0.848
## F-statistic: 280 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP12" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.487 -3.027 2.848 5.979 17.924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 521.48680 2.72940 191.06 <0.0000000000000002 ***
## op_count 1.63190 0.09408 17.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.89 on 49 degrees of freedom
## Multiple R-squared: 0.86, Adjusted R-squared: 0.8571
## F-statistic: 300.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP13" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.857 -2.760 4.479 6.853 13.133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 522.66365 2.84803 183.52 <0.0000000000000002 ***
## op_count 1.69385 0.09817 17.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.32 on 49 degrees of freedom
## Multiple R-squared: 0.8587, Adjusted R-squared: 0.8558
## F-statistic: 297.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP14" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.586 -2.595 2.848 5.848 12.053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 522.93854 2.61641 199.87 <0.0000000000000002 ***
## op_count 1.64756 0.09019 18.27 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.48 on 49 degrees of freedom
## Multiple R-squared: 0.872, Adjusted R-squared: 0.8694
## F-statistic: 333.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP15" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.057 -1.980 2.391 6.036 12.222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 519.43967 2.64511 196.38 <0.0000000000000002 ***
## op_count 1.61692 0.09117 17.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.584 on 49 degrees of freedom
## Multiple R-squared: 0.8652, Adjusted R-squared: 0.8625
## F-statistic: 314.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP16" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.698 -2.365 3.358 5.830 12.257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 538.69796 2.85948 188.39 <0.0000000000000002 ***
## op_count 1.64973 0.09856 16.74 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.36 on 49 degrees of freedom
## Multiple R-squared: 0.8511, Adjusted R-squared: 0.8481
## F-statistic: 280.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP1" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.3398 -2.6690 -0.4617 2.6236 9.9651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 320.04713 1.02181 313.22 <0.0000000000000002 ***
## op_count 2.52439 0.03522 71.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.702 on 49 degrees of freedom
## Multiple R-squared: 0.9906, Adjusted R-squared: 0.9904
## F-statistic: 5137 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP2" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5042 -1.6168 -0.0011 1.2335 7.0545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 317.31900 0.81103 391.25 <0.0000000000000002 ***
## op_count 2.44136 0.02796 87.33 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.939 on 49 degrees of freedom
## Multiple R-squared: 0.9936, Adjusted R-squared: 0.9935
## F-statistic: 7627 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP3" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4542 -2.6289 -0.9463 1.8731 7.7362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 318.95814 0.88677 359.7 <0.0000000000000002 ***
## op_count 2.40873 0.03057 78.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.213 on 49 degrees of freedom
## Multiple R-squared: 0.9922, Adjusted R-squared: 0.992
## F-statistic: 6210 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP4" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.9922 -3.1373 -0.3989 2.2747 7.9871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 319.3782 1.0589 301.61 <0.0000000000000002 ***
## op_count 2.5104 0.0365 68.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.837 on 49 degrees of freedom
## Multiple R-squared: 0.9897, Adjusted R-squared: 0.9895
## F-statistic: 4730 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP5" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.2759 -1.9118 0.1695 2.1155 6.6218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 318.86689 0.87527 364.31 <0.0000000000000002 ***
## op_count 2.56023 0.03017 84.86 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.171 on 49 degrees of freedom
## Multiple R-squared: 0.9932, Adjusted R-squared: 0.9931
## F-statistic: 7201 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP6" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3333 -2.3848 -0.5686 1.9093 9.3284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 321.81863 0.91290 352.52 <0.0000000000000002 ***
## op_count 2.45588 0.03147 78.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.308 on 49 degrees of freedom
## Multiple R-squared: 0.992, Adjusted R-squared: 0.9919
## F-statistic: 6091 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP7" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0068 -2.5101 -0.5097 2.7415 7.9907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 328.51320 1.06616 308.13 <0.0000000000000002 ***
## op_count 2.49986 0.03675 68.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.863 on 49 degrees of freedom
## Multiple R-squared: 0.9895, Adjusted R-squared: 0.9893
## F-statistic: 4627 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP8" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8839 -1.6780 -0.2345 1.8051 13.8899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 320.88386 1.05934 302.91 <0.0000000000000002 ***
## op_count 2.51131 0.03651 68.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.838 on 49 degrees of freedom
## Multiple R-squared: 0.9897, Adjusted R-squared: 0.9895
## F-statistic: 4730 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP9" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8460 -2.4921 -0.7775 1.9083 15.4694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 320.79827 1.20200 266.89 <0.0000000000000002 ***
## op_count 2.56846 0.04143 61.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.355 on 49 degrees of freedom
## Multiple R-squared: 0.9874, Adjusted R-squared: 0.9872
## F-statistic: 3843 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP10" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.4906 -3.7293 -0.8214 2.2406 21.1880
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 331.13348 1.55859 212.46 <0.0000000000000002 ***
## op_count 2.61466 0.05372 48.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.647 on 49 degrees of freedom
## Multiple R-squared: 0.9797, Adjusted R-squared: 0.9793
## F-statistic: 2369 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP11" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1051 -3.2954 0.1944 1.9686 13.5777
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 320.4148 1.2389 258.63 <0.0000000000000002 ***
## op_count 2.3909 0.0427 55.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.489 on 49 degrees of freedom
## Multiple R-squared: 0.9846, Adjusted R-squared: 0.9843
## F-statistic: 3135 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP12" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4487 -2.4381 -0.6198 1.7750 15.5619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 325.15913 1.03716 313.51 <0.0000000000000002 ***
## op_count 2.36579 0.03575 66.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.758 on 49 degrees of freedom
## Multiple R-squared: 0.9889, Adjusted R-squared: 0.9887
## F-statistic: 4379 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP13" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9778 -2.8414 0.3338 2.8206 9.5503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 325.97775 1.15361 282.57 <0.0000000000000002 ***
## op_count 2.54050 0.03976 63.89 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.18 on 49 degrees of freedom
## Multiple R-squared: 0.9881, Adjusted R-squared: 0.9879
## F-statistic: 4082 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP14" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.534 -3.026 -0.557 3.178 9.414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 319.11727 1.14505 278.69 <0.0000000000000002 ***
## op_count 2.49805 0.03947 63.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.149 on 49 degrees of freedom
## Multiple R-squared: 0.9879, Adjusted R-squared: 0.9877
## F-statistic: 4006 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP15" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9502 -2.5611 -0.4977 2.7095 11.0213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 338.95023 1.23997 273.35 <0.0000000000000002 ***
## op_count 2.50317 0.04274 58.57 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.493 on 49 degrees of freedom
## Multiple R-squared: 0.9859, Adjusted R-squared: 0.9856
## F-statistic: 3430 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP16" "revm"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.4419 -3.2526 -0.5228 3.2609 11.8960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 331.44193 1.23791 267.74 <0.0000000000000002 ***
## op_count 2.43919 0.04267 57.16 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.485 on 49 degrees of freedom
## Multiple R-squared: 0.9852, Adjusted R-squared: 0.9849
## F-statistic: 3268 on 1 and 49 DF, p-value: < 0.00000000000000022
Export the results
write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)